基于SAO-BiLSTM-KAN的电池健康状态估计OA

To improve the estimation accuracy of the state of health(SOH)for lithium-ion batteries,a novel estima-tion method based on snow ablation optimization,bidirectional long short-term memory network and Kolmogorov-Arnold networks(SAO-BiLSTM-KAN)is proposed.Firstly,data is extracted from the battery charging process,and health features are derived through incremental capacity analysis.These features are then fed into a BiLSTM network to capture long-term dependencies within the time series.Subsequently,the outputs from the BiLSTM are passed to the KAN to explore complex nonlinear relationships among the features,thereby enhancing estimation performance.To achieve optimal results,the SAO algorithm is introduced to optimize the model's hyperparameters.Experimental results demonstrate that the proposed model delivers outstanding performance across various comparative tests,with both the root mean square error(RMSE)and mean absolute error(MAE)for SOH estimation remaining below 0.919%,validating the method's superior prediction accuracy and generalization capability.